Women are particularly underrepresented as leading authors of papers in journals of the highest impact factor, with substantial consequences for their careers. While a large body of research has focused on the outcome and the process of peer review, fewer articles have explicitly focused on gendered submission behavior and the explanations for these differences. In our study of nearly 5000 active authors, we find that women are less likely to report having submitted papers to journals of the highest impact (e.g., Science, Nature, or PNAS) and to submit fewer manuscripts, on average, than men when they do submit. Women were more likely to indicate that they did not submit their papers (in general and their subsequently most cited papers) to high-impact journals because they were advised not to. In the aggregate, no statistically significant difference was observed between men and women in how they rated the quality of their work. Nevertheless, regardless of discipline, women were more likely than men to indicate that their ‘work was not ground-breaking or sufficiently novel’ as a rationale for not submitting to one of the listed prestigious journals. Men were more likely than women to indicate that the ‘work would fit better in a more specialized journal’. We discuss the implications of these findings and interventions that can serve to mitigate the disparities caused by gendered differences in submission behavior.
scholarly publishingpeer reviewgender gapimpact factorResearch organismNonehttps://ror.org/0517h6h17Canada Research ChairsLarivièreVincentThe funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.Author impact statementGender differences in submission behavior, driven by self-perceptions of novelty and peer discouragement, limit women’s representation in high-impact journals and, in turn, their scientific visibility and career advancement.publishing-routeprcIntroduction
The rise of the research evaluation system has created a market of intense competition for a few hallowed venues. Scientific capital is strongly concentrated in generalist journals founded in the late 19th and early 20th centuries (Baldwin, 2015), such as Science (1880), Nature (1869), and Proceedings of the National Academy of Sciences of the United States of America (PNAS, 1914). Articles published in these journals garner greater news media attention, and authors tend to receive more career opportunities and grants (Samuel Reich, 2013). Since publishing in highly influential journals leads to wider dissemination and visibility of researchers’ work, the volume of manuscript submissions to these journals is high and acceptance low, with a large proportion of manuscripts rejected by editors without undergoing peer review. It is noted on Science’s author portal that less than 6% of originally submitted papers are accepted (Science, 2025). Nature also notes low acceptance rates on its website, from 8% to 12% depending on the year, and states that ‘many submissions are declined without being sent for review’ (Nature, 2025). A similar practice is espoused by the Proceedings of the National Academy of Sciences of the United States of America (PNAS), with most publications rejected before peer review and a final acceptance rate of 14.7% (PNAS, 2025). Prestige and hierarchy among journals are not inherently negative for scholarly communication; however, if entry into these highly visible venues varies for different sociodemographic populations of authors, it may have adverse consequences for science (Kozlowski et al., 2022).
Editors and peer reviewers evaluate both the quality of manuscripts submitted as well as whether the topic fits within the scope of the journal. Idealized gatekeeping reflects the Mertonian norms of organized skepticism and universalism, evaluating scientific work independently of the social characteristics of authors (Merton, 1968). However, various critiques of scholarly peer review have been discussed (Ware, 2008), among which is a concern of social biases, that is, ‘differential evaluation of an author’s submission as a result of her/his perceived membership in a particular social category’ (Lee et al., 2013). These biases are not necessarily conscious; yet, if no biases were present in the case of peer review, then ‘we should expect the rate with which members of less powerful social groups enjoy successful peer review outcomes to be proportionate to their representation in submission rates’ (Lee et al., 2013). Results on gender disparities and bias in scholarly publishing have arrived at mixed results (Borsuk et al., 2009; Day et al., 2020; Djupe et al., 2019; Edwards et al., 2018; Fox and Paine, 2019; Gilbert et al., 1994; Grossman, 2020; Helmer et al., 2017; Murray et al., 2018; Squazzoni et al., 2021; Walker et al., 2015). However, many of these fail to disentangle components of the publication process, focusing only on the outcome of peer-reviewed publications rather than the effect of different groups having different submission rates or desk rejections. These components have fundamentally different attributes—with one being an effect of self-selection and the other a potential indicator of bias. We emphasize here that observations of disparities are a necessary, but insufficient indicator of bias as there are several other potential explanations for disparities. We focus, therefore, on the concept of disparity.
Most studies focused on disparities in science publishing have demonstrated that women submit fewer papers than men, with little difference in desk rejections (or a disparity in favor of women) (Bendels et al., 2018; Garand and Harman, 2021; Grossman, 2020; Martinsen et al., 2022; Squazzoni et al., 2021). The lower submission rate is particularly pronounced in the most prestigious journals (Bendels et al., 2018). Interviews with women authors explained these lower submission rates with higher selectivity: they argued that they only submit when they are confident the paper would be accepted, not wanting to risk a rejection as they have limited time for conducting research and attending to lengthy reviews (Closa et al., 2020). Women were also less likely than men to indicate that they submit their manuscripts to top journals in their field as their first choice and more likely than men to select a journal based on whether a journal is most likely to accept the manuscript (Djupe et al., 2019). Other perceptions and behavioral differences have also been postulated to account for the differences in manuscript submission behaviors, including risk aversion, perfectionism (Borghans et al., 2009; Closa et al., 2020; Djupe et al., 2019), and assessment of contribution (Lincoln et al., 2012; Wennerås and Wold, 2008). However, few large-scale studies have examined this question from the perspective of the authors. In this context, our paper seeks to address whether the sociodemographic characteristics of lead authors influence submission rates to elite journals. More specifically, based on a survey of more than 4700 active authors (29.9% women), we assess differences in gender, academic rank, and discipline in the likelihood of submitting to Science, Nature, and PNAS and the self-reported reasons for not submitting to these journals.
ResultsJournal submission behavior
Women were less likely than men to report that they submitted a paper to Science, Nature, or PNAS (48.7% of men and 37.0% of women, see Appendix 1—table 7). These differences are statistically significant (see Appendix 1—table 8), with men, in general, being more likely to submit to Science, Nature, or PNAS than women while controlling for academic rank (OR = 0.61, 95% CI [0.54, 0.70]). This relationship remains significant for each discipline (see Figure 1), with relatively higher percentages of men in each of the disciplines submitting to these journals (ranging from 44.2% to 53.4%), compared to women (ranging from 33.5% to 37.5%). We also investigated the mean number of manuscripts that respondents reported submitting to Science, Nature, or PNAS (see Figure 1 and Appendix 1—table 9). All disciplines combined, 552 women and 1583 men submitted to these three elite journals. The mean was calculated by investigating only respondents who submitted at least one manuscript to Science, Nature, or PNAS. The number of submissions an author could have submitted depends on the discipline (due to the difference in publication behavior between disciplines) and career status (junior, senior, non-academic). In medical sciences (coefficient = −1.46, 95% CI [−2.14, –0.79], p = 0.00) and natural sciences and engineering (coefficient = −0.80, 95% CI [−1.39, –0.22], p = 0.01) women submitted fewer manuscripts than men. No statistically significant difference was observed for the social sciences (coefficient = −1.35, 95% CI [–3.00, 0.31], p = 0.11, see Appendix 1—table 10).
Measures of submission rate, acceptance rate, and rejection rate with tests of statistical significance.
Logistic regression was used for whether they submitted to an elite journal by individual discipline. Linear regression (mixed-effect model) was used to understand the gender difference in number of submissions, acceptance rate, and desk rejection rate to these journals by individual discipline. We controlled for rank in all regression analysis. Error bars represent the 95% confidence interval. All odds ratio and coefficient values are based on women over men. MS: Medical Sciences; NSE: Natural Sciences and Engineering; SS: Social Sciences. ** indicates p < 0.01, * indicates p < 0.05.
Self-reported acceptance and rejection rate
We examined the difference between women and men regarding the self-reported acceptance and rejection rates for Science, Nature, and PNAS, using linear regression analysis while controlling for academic rank. The result shows no significant difference between women and men in getting their papers accepted (see Figure 1, Appendix 1—table 11, and Appendix 1—table 12). We also used a mixed-effect model to analyze the role of gender in the chances of getting manuscripts accepted, which yields similar conclusions to those above. We defined the desk rejection rate as the number of manuscripts that did not go out for peer review divided by the number of manuscripts submitted for each survey respondent. The relationship between desk rejection rate and gender was examined using linear regression and a mixed-effect model, again controlling for rank. Both models yield similar results: gender did not significantly impact the possibility of getting a desk rejection for manuscripts submitted to these journals, as illustrated in Figure 1 (as well as Appendix 1—table 13 and Appendix 1—table 14).
Submission and desk rejections of authors’ highest cited papers
To understand whether the gender differences observed above also hold for the subset of higher-impact research, we investigated respondents' submission pattern for their most cited papers (not published in Science, Nature, PNAS, but also Cell, Nature Communications, NEJM, and Science Advances; see Materials and methods for more details). Results show that a substantial proportion of the authors (48.6%) considered other journals than the one in which their paper was published, with no significant gender difference (see Appendix 1—table 17 and Appendix 1—table 18). However, only a small proportion of authors (9.1%) considered Science, Nature, or PNAS, again with no significant gender difference (see Appendix 1—table 19 and Appendix 1—table 20). As only a few authors considered publishing in these journals, it is not surprising to see that few authors submitted their most cited paper to Science, Nature, or PNAS (228 respondents), again with no significant gender gap (see Appendix 1—table 21 and Appendix 1—table 22). Similarly, no significant difference was observed in desk rejection rate for their most cited paper between men and women in any of the disciplines when submitting to Science, Nature, and PNAS (see Appendix 1—table 23 and Appendix 1—table 24). Submission behavior and the chance of getting a desk rejection for higher quality papers thus do not seem to differ significantly between men and women (Figure 2), although these conclusions are based on a very limited number of responses: 480 women and 1404 men across all disciplines.
Measures of journal consideration, submission rate, and desk rejection rate for most cited papers with odds ratios (women/men).
Error bars represent the 95% confidence interval. We used logistic regression to analyze the relationship between gender and the four variables, while controlling for rank. Regression was done by each discipline separately. MS: Medical Sciences; NSE: Natural Sciences and Engineering; SS: Social Sciences; SNP: Science, Nature, PNAS.
Perception of the quality of research
Respondents were asked to rate their perception of the quality of their research (not their papers) in comparison to their peers on a five-point Likert scale ranging from Excellent (5) to Poor (1). Most respondents rated their work as good or excellent (86.7%), regardless of discipline, with very few rating their work as poor or fair (1.0%), and only a few respondents rated it as average (12.4%) (Figure 3 and Appendix 1—table 27). When considering responses regardless of discipline, no statistically significant difference was observed between men and women regarding how they rated the quality of their work (see Appendix 1—table 28). When disaggregating the respondents by discipline, it was found that compared with men, women in the natural sciences and engineering are more likely to rank their research quality lower (OR = 0.83, 95% CI [0.67, 0.99], p = 0.04). No statistically significant difference was observed in the medical (OR = 1.18, 95% CI [0.99, 1.40], p = 0.07) and social sciences (OR = 1.13, 95% CI [0.93, 1.22], p = 0.58).
Odds ratio (women/men) and mean value for research quality comparison with peers.
Ordinal logistic regression was used to analyze the relationship between gender and the rating of research quality while controlling for respondents’ rank. Regression was done at the discipline level. Error bars represent the 95% confidence interval. MS: Medical Sciences; NSE: Natural Sciences and Engineering; SS: Social Sciences. * indicates p < 0.05.
Reasons for not submitting to elite journals
We also investigated the authors’ rationale for why they did not submit their manuscripts (in general and their most cited) to the selected journals by asking respondents to select from a list of potential reasons. The results of the regression analysis are represented in Figure 4. None of the respondents with a known academic rank indicated that they did not submit to the journals because they were unaware of the journals. The most common reason men and women gave was that their work would fit better in a more specialized journal.
Reasons for not submitting to top journals, overall and most cited paper, with odds ratios (women/men).
Logistic regression was employed to analyze the relationship between gender and each of the reasons for not submitting. Regression was done by reason and discipline while controlling for rank. Error bars represent the 95% confidence interval. MS: Medical Sciences; NSE: Natural Sciences and Engineering; SS: Social Sciences. ** indicates p < 0.01, * indicates p < 0.05.
Two of the listed reasons are related to the quality and the novelty of the research. Women in natural sciences and engineering were more likely than men to indicate that ‘the work was not of high enough quality’ as one of the reasons why they did not submit to Science, Nature, or PNAS (OR = 1.59, 95% CI [1.22, 2.08], p = 0.001). A similar finding was also found for the most cited paper, although only significant in the medical sciences (OR = 0.75, 95% CI [0.60, 0.94], p = 0.011). Regardless of discipline, women were more likely than men to (OR = 1.26, 95% CI [1.07, 1.50]) indicate that they consider their ‘work was not ground-breaking or sufficiently novel’ for the journal in question. For their most cited papers, it was also the case for authors publishing in natural sciences and engineering (OR = 1.36, 95% CI [1.10, 1.67], p = 0.004), as well as all disciplines combined (OR = 1.19, 95% CI [1.04, 1.37], p = 0.011) (see Appendix 1—table 16 and Appendix 1—table 26).
Reasons related to the scope and audience of the journals mostly show non-significant gender differences, with one exception. In general, men were more likely than women to indicate that the ‘work would fit better in a more specialized journal’ for their general submissions (OR = 0.72, 95% CI [0.60, 0.85], p = 0.000). When disaggregated by discipline, a significant difference was observed both for the medical sciences (OR = 0.74, 95% CI [0.57, 0.95], p = 0.018) and for the natural sciences and engineering (OR = 0.72, 95% CI [0.55, 0.95], p = 0.018). However, no statistically significant difference was observed for the most cited papers. Men in the medical sciences were also more likely than women to indicate that the ‘work would reach a wider audience in another journal’ (OR = 0.61, 95% CI [0.41, 0.92], p = 0.017). No statistically significant difference was observed for the most cited papers. In contrast to the above results, women submitting in the social sciences were more likely to indicate that the ‘work fell out of the scope of the journal’ (OR = 1.84, 95% CI [1.03, 3.28], p = 0.04).
The last two reasons are much less frequent and relate to the wishes of the co-authors and the advice received. In the natural sciences and engineering, women were more likely than men to indicate that their co-authors wished to submit elsewhere (OR = 1.70, 95% CI [1.06, 2.74], p = 0.029). This was also the case for their most cited paper (OR = 1.57, 95% CI [1.02, 2.40], p = 0.039). Without disaggregating by discipline, women were more likely to indicate that they did not submit their papers (in general and their most cited papers) to Science, Nature, or PNAS because they were advised not to. When disaggregating by discipline, this difference was also observed for respondents in natural sciences and engineering for their papers in general (OR = 1.69, 95% CI [1.02, 2.81], p = 0.042) and for medical sciences respondents for their most cited papers (OR = 1.75, 95% CI [1.05, 2.93], p = 0.032).
Discussion
Women are particularly underrepresented in journals with the highest impact factor (Huang et al., 2020), with substantial consequences for their careers. While a large body of research has focused on the outcome and the process of peer review, fewer articles have explicitly focused on gendered submission behavior and the explanations for these differences. In our study of nearly 5000 active authors, we find that women are less likely to report having submitted papers and, when they have, to submit fewer manuscripts, on average, than men. This reinforces much of the literature demonstrating lower submission rates among women (Bendels et al., 2018; Grossman, 2020; Martinsen et al., 2022; Squazzoni et al., 2021). One strategy might be to optimize around homophily: women editors tend to disproportionately select women reviewers (Helmer et al., 2017), and women reviewers are more favorable to women-authored work (Murray et al., 2018). The positive reinforcement in the process will increase women reviewers and authors, which may influence submission behavior on the broader network. Another strategy might be to introduce quotas into peer review; however, this has been shown to have negative effects on women (Leibbrandt et al., 2018).
There is a demonstrated relationship between self-efficacy and publication output (Hemmings and Kay, 2009); however, our results on this were mixed. In the aggregate, no statistically significant difference was observed between men and women in how they rated the quality of their work. A difference was only observed in the natural sciences and engineering, where women ranked their research quality lower. The latter is particularly interesting, given that women tend to outperform in Engineering on several indicators (Ghiasi et al., 2015). The weak signal observed suggests that no particular intervention may be necessary to improve self-efficacy for this population; however, there may be some internalization of value premised on past experiences in peer review. Therefore, addressing larger issues of bias in evaluation (Lee et al., 2013) may serve to mitigate any gendered differences in self-efficacy.
Regardless of discipline, women were more likely than men to indicate that their ‘work was not ground-breaking or sufficiently novel’ for the listed prestigious journals. Men were more likely than women to indicate that the ‘work would fit better in a more specialized journal’. Women were more likely to indicate that they did not submit their papers (in general and their most cited papers) to Science, Nature, or PNAS because they were advised not to. These results may reinforce the notion of risk aversion and perfectionism (Closa et al., 2020), suggesting that women have a higher internal standard for what constitutes novel research. However, the more alarming result is that women seem to be deterred from submitting from people within their scholarly network. One interpretation could be that the work is truly not sufficiently novel; this would suggest that men are innately predisposed to higher novelty research designs or that they are trained in this manner. Given no evidence of the former and sufficient evidence of differential mentoring for women (Nolan et al., 2008), it would stand to reason that greater attention should be made to equity in research training. However, the alternative interpretation is that women are not receiving the simple encouragement necessary to seek out higher-impact venues. Research administrators may seek simple interventions to increase this support. Journal editors may also want to consider dedicated outreach to women-led labs.
Addressing gender disparities in science requires a multifaceted approach that considers all components of the scientific system. In that context, we cannot overemphasize the need for a research evaluation reform—including initiatives that span from the Leiden Manifesto to the San Francisco Declaration on Research Assessment. But until those reforms are actually adopted by universities and funders and actions move beyond pledges, publishing remains a cornerstone of this system and central to research evaluation. Therefore, addressing mechanisms that create disparities in journal submission—be they self-imposed or otherwise—is essential for creating a robust and responsible research ecosystem.
Materials and methods
A survey was conducted to explore the relationship between author gender and manuscript submission, rejection, and acceptance rates for high-impact, multidisciplinary journals in physical sciences, life sciences, and engineering (see S.I. appendix for the questionnaire). This study focuses on the results for Science, Nature, and PNAS. The target population consisted of active authors who published articles from 2008 to 2017 in journals indexed by WoS in medical sciences, natural sciences and engineering, and social sciences (Appendix 1—table 2). The questionnaire asked respondents to report on their manuscript submission experiences. It included a subsection focusing on their most cited paper published after 2010 not published in selected elite journals, that is, Science, Nature, PNAS (which are analyzed in the main manuscript), as well as Cell, Nature Communications, NEJM, and Science Advances (which are analyzed in the S.I. appendix). Specific questions addressed their experiences submitting their most cited manuscripts to these journals, as well as their experiences submitting to these journals in general. Stata software (Standard Edition 17) was used to conduct the analyses, which consisted of applying ordinal logistic regression, multinomial logistic regression, and multiple logistic regression with a significance level or alpha of 0.05. Although we collected data on genders other than men and women, the independent variable studied is a binary gender variable (i.e., women or men), given the low number of respondents outside these two categories. Respondents were disaggregated by academic rank (i.e., junior, senior, or non-academic, excluding all unknowns) and discipline (i.e., social sciences, medical sciences, and natural sciences and engineering). More details about the regression analysis can be found in the appendix. The sample consisted of 6002 respondents who participated in the survey, of whom 4857 finished the questionnaire. An analysis of the attrition failed to identify a common point of departure, suggesting individual variability in dropout rather than failed survey construction. The final number of respondents in this study is 4805, after the removal of 19 responses in Arts and Humanities and additional responses due to a lack of information for critical variables. Of the final number of respondents, 4740 (98.6%) have a known academic rank and are included in the regression analysis (more details available in the S.I. Appendix 1—table 6).
Additional informationCompeting interests
No competing interests declared
Author contributions
Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Writing – review and editing
Investigation, Writing – original draft, Writing – review and editing
Conceptualization, Methodology, Writing – review and editing
Conceptualization, Methodology, Writing – review and editing
Conceptualization, Methodology, Writing – original draft, Writing – review and editing
Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
Ethics
This analysis was approved by McGill University's Research Ethics Board II, R.E.B. File #: 501-0518 Factors affecting scientific manuscript submission and acceptance rates.
Additional filesData availability
All data needed to evaluate the conclusions in the paper are present in the paper and the Supporting information. Aggregated, de-identified data by gender, discipline, and rank for analyses are available on GitHub (https://github.com/MetascienceLab/genderPublishing/ copy archived at Metascience Research Lab, 2024). Due to Institutional Review Board restrictions and the terms of participant consent, the survey data used in this study cannot be shared publicly. However, to support transparency and reproducibility, all analysis code and a synthetic dataset- constructed with randomly generated values but matching the original data structure- are available on Zenodo: https://doi.org/10.5281/zenodo.16327580.
The following dataset was generated:
NiCBassonIBadiaGTufenkjiNSugimotoCRLarivièreV2025Data for Gender differences in submission behavior exacerbate publication disparities in elite journalsZenodo10.5281/zenodo.16327580
Acknowledgements
Vincent Larivière acknowledges funding from the Canada Research Chairs program. The authors would like to thank the survey respondents and the Indiana University Center for Survey Research for their critical contribution to the study. This analysis was approved by McGill University’s Research Ethics Board II, R.E.B. File #: 501-0518 Factors affecting scientific manuscript submission and acceptance rates.
ReferencesBaldwinM2015Making “Nature”: The History of a Scientific JournalUniversity of Chicago PressBendelsMHKMüllerRBrueggmannDGronebergDA2018Gender disparities in high-quality research revealed by Nature Index journalsPLOS ONE13e018913610.1371/journal.pone.018913629293499BorghansLGolsteynBHHHeckmanJJMeijersH2009Gender differences in risk aversion and ambiguity aversionJournal of the European Economic Association764965810.1162/JEEA.2009.7.2-3.649BorsukRMAarssenLWBuddenAEKorichevaJLeimuRTregenzaTLortieCJ2009To name or not to name: the effect of changing author gender on peer reviewBioScience5998598910.1525/bio.2009.59.11.10ClosaCMouryCNovakovaZQvortrupMRibeiroB2020Mind the (submission) gap: EPSR gender data and female authors publishing perceptionsEuropean Political Science1942844210.1057/s41304-020-00250-5DayAECorbettPBoyleJ2020Is there a gender gap in chemical sciences scholarly communication?Chemical Science112277230110.1039/c9sc04090k32180933DjupePASmithAESokheyAE2019Explaining gender in the journals: how submission practices affect publication patterns in political sciencePS: Political Science & Politics52717710.1017/S104909651800104XEdwardsHASchroederJDugdaleHL2018Gender differences in authorships are not associated with publication bias in an evolutionary journalPLOS ONE13e020172510.1371/journal.pone.020172530157231FoxCWPaineCET2019Gender differences in peer review outcomes and manuscript impact at six journals of ecology and evolutionEcology and Evolution93599361910.1002/ece3.499330962913GarandJCHarmanM2021Journal desk-rejection practices in political science: bringing data to bear on what journals doPS: Political Science & Politics5467668110.1017/S1049096521000573GhiasiGLarivièreVSugimotoCR2015On the compliance of women engineers with a gendered scientific systemPLOS ONE10e014593110.1371/journal.pone.014593126716831GilbertJRWilliamsESLundbergGD1994Is there gender bias in JAMA’s peer review process?JAMA2721391428015126GrossmanE2020A gender bias in the European Journal of Political Research?European Political Science1941610.1057/s41304-020-00252-3HelmerMSchottdorfMNeefABattagliaD2017Gender bias in scholarly peer revieweLife6e2171810.7554/eLife.2171828322725HemmingsBKayR2009Lecturer self efficacy: Its related dimensions and the influence of gender and qualificationsIssues in Educational Research19243254HuangJGatesAJSinatraRBarabásiAL2020Historical comparison of gender inequality in scientific careers across countries and disciplinesPNAS1174609461610.1073/pnas.1914221117KozlowskiDLarivièreVSugimotoCRMonroe-WhiteT2022Intersectional inequalities in sciencePNAS119e211306711910.1073/pnas.2113067119LeeCJSugimotoCRZhangGCroninB2013Bias in peer reviewJournal of the American Society for Information Science and Technology6421710.1002/asi.22784LeibbrandtAWangLCFooC2018Gender quotas, competitions, and peer review: experimental evidence on the backlash against womenManagement Science643501351610.1287/mnsc.2017.2772LincolnAEPincusSKosterJBLeboyPS2012The matilda effect in science: awards and prizes in the US, 1990s and 2000sSocial Studies of Science4230732010.1177/030631271143583022849001MartinsenDSGoetzKHMüllerWC2022A gendered pattern? Publishing, submission and reviewing in West European PoliticsEuropean Political Science2143044210.1057/s41304-021-00356-4MertonRK1968The matthew effect in scienceScience159566310.1126/science.159.3810.56Metascience Research Lab2024genderPublishingswh:1:rev:d6d8cdd12dacdb1bd15d1b1df8cc7f83b2bb69cfSoftware Heritagehttps://archive.softwareheritage.org/swh:1:dir:428cca15d19a7100aa16a9e4abd2f4365b123bbc;origin=https://github.com/MetascienceLab/genderPublishing;visit=swh:1:snp:bb379b9bcbbd9da187f9d218f2592560f7bc6d75;anchor=swh:1:rev:d6d8cdd12dacdb1bd15d1b1df8cc7f83b2bb69cfMurrayDSilerKLarivièreVChanWMCollingsAMRaymondJSugimotoCR2018Gender and international diversity improves equity in peer reviewbioRxiv10.1101/400515Nature2025Editorial criteria and processes of Naturehttps://www.nature.com/nature/for-authors/editorial-criteria-and-processesDecember 12, 2025NiCSmithEYuanHLarivièreVSugimotoCR2021The gendered nature of authorshipScience Advances710.1126/sciadv.abe4639NolanSABucknerJPMarzabadiCHKuckVJ2008Training and mentoring of chemists: a study of gender disparitySex Roles5823525010.1007/s11199-007-9310-5PNAS2025Editorial and journal policieshttps://www.pnas.org/about/article-journal-metricsDecember 12, 2025Samuel ReichE2013Science publishing: The golden clubNature50229129310.1038/502291aScience2025Science contributors FAQhttps://www.science.org/content/page/science-contributors-faqDecember 12, 2025SquazzoniFBravoGFarjamMMarusicAMehmaniBWillisMBirukouADondioPGrimaldoF2021Peer review and gender bias: a study on 145 scholarly journalsScience Advances7eabd029910.1126/sciadv.abd029933523967WalkerRBarrosBConejoRNeumannKTelefontM2015Personal attributes of authors and reviewers, social bias and the outcomes of peer review: a case studyF1000Research42110.12688/f1000research.6012.2WareM2008Peer review in scholarly journals: perspective of the scholarly community – results from an international studyInformation Services & Use2810911210.3233/ISU-2008-0568WenneråsCWoldA2008Nepotism and sexism in peer-reviewWenneråsCWoldAWomen, Science, and TechnologyRoutledge17Appendix 1Expansion on materials and methodsSampling frame construction
The sampling frame was constructed using data extracted from Web of Science (WoS), as hosted by the Centre for Science and Technology Studies (CWTS) at Leiden University. Using the author disambiguation algorithm developed by Caron and van Eck (2014) (Caron, E. & Van Eck, N.J. (2014). "Large scale author name disambiguation using rule-based scoring and clustering." in Proceedings of the 19th international conference on science and technology indicators, Leiden), we extracted metadata of all publications for authors who have been corresponding authors of at least one paper between 2008 and 2017. The metadata included the email addresses of these authors; these email addresses were used to contact the participant population. A minimum cut-off point of 10 papers for the identification of prolific authors was selected. When considering whether an author is a prolific author, only research papers were considered. The total study population consists of 494,781 prolific authors, data on the gender and discipline in which they publish were known for 415,697 authors (84.0%). The gender of an author for the sampling frame was determined by the algorithm developed in Larivière et al. (2013) (Larivière, V., Ni, C., Gingras, Y., Cronin, B. & Sugimoto, C.R. (2013). "Bibliometrics: Global gender disparities in science." Nature, 504: 211–213. doi:10.1038/504211a) whereas the gender variable used in the analysis was determined by the responses of the authors. The discipline in which an author publishes was determined by the WoS journal classification.
Identifying most cited papers
Several questions in the questionnaire ask the authors about their decisions pertaining to their most cited paper. The questionnaires are customized to each respondent, showing them the title of their most cited paper when these questions are asked. The most cited paper was identified from the previous corpus of papers by the author published between 2008 and 2017, that has received the most citations at the time of data extraction and were not published in the selected elite journals. Despite the fact that citations take time to accumulate, the skewed nature of citation distributions—where most papers remain lowly cited, and a few papers get highly cited—leads to a situation where most cited papers are not necessarily the oldest papers published by respondents.
Elite journal selection
The questionnaire mainly consists of questions related to the author’s submission behavior as they pertain to elite journals. The seven journals were chosen given their general reputation and research impact. All seven journals were in the top five journals with the highest impact factors in the 2016 Journal Citation Reports categories of ‘Multidisciplinary sciences’, ‘General & internal medicine’, and ‘Biology’. Journal Citation Reports (more information at https://clarivate.libguides.com/jcr) (https://jcr.clarivate.com/jcr/) is a resource published annually by Clarivate Analytics that provides the impact factor for all journals included in WoS. The journal impact factor (Garfield, E. (2006). “The history and meaning of the journal impact factor.” JAMA, 295(1):90–93. doi:10.1001/jama.295.1.90) is a calculation that employs citations that articles in a journal received as an indication of the influence of that journal in its field.
Regression analysis
This study used regression analysis to analyze the data. The analysis described here is similar to Ni et al., 2021 and how it is reported in its supplementary materials. Here, the discussion is slightly adapted to reflect the variables and questions used within this study. The regression procedures include binary logistic regression, ordinal logistic regression, linear regression, and mixed-effects model. Regression analysis is usually used to explore relationships between dependent and independent variables. The most common is linear regression, in which:Yi=β0+β1Xi+β2Zi+⋯+ei\begin{document}$$\displaystyle {Y_i = \beta_0 + \beta_1 X_i + \beta_2 Z_i + \cdots + e_i}$$\end{document}
where with one unit change in X there is β1 differences in Y after Z and other variables are controlled. However, this approach assumes that all dependent variables are normally distributed. Furthermore, it often assumes that the dependent variable Y is continuous. Our dataset, like many social science surveys, is replete with categorical variables. We want to understand whether there is a gendered difference when selecting one category over another. Therefore, we turned to logistic regression. Specific procedures and analysis methods vary by the scale of dependent variables, as well as the number of variable categories.
For questions with answers measured in ordinal scales (e.g., Likert scaling), ordinal logistic regression analysis was employed. For questions with answers (the dependent variables) presented in a categorical, unordered scale, binary logistic regression (when the dependent variable only contains two categories) was performed. The point statistic was still the odds ratio of women over men. Here again, gender is the independent variable, with the academic rank being the controlled variable in the case when a dependent variable only has two categories (e.g., ‘yes’ or ‘no’ to an answer), binary logistic regression analysis was conducted to analyze the data. An odds ratio value significantly over 1 indicates that women were more likely than men to select ‘Yes’ for this question. A separate regression analysis is performed for each discipline with rank being controlled. For analyses concerning all disciplines, we control for both academic rank and discipline.
The binary logistic regression model for each discipline can be described as follows:Yi=Log(p1−p)=β0+β1Xi+β2Zi+⋯+ei\begin{document}$$\displaystyle Y_i = {\rm Log} \left(\frac{p}{1-p}\right) =\beta_0 + \beta_1 X_i + \beta_2 Z_i + \cdots + e_i$$\end{document}
where Yi is the log probability of the binary dependent variable (such as whether you submitted to a journal), Xi is the binary independent variable gender (woman = 1 and man = 0), and Zi is the other control variable(s).
The mixed-effect linear model for each discipline can be described as follows:Yi=β0+β1Xi+Zi+⋯+ei\begin{document}$$\displaystyle {Y_i = \beta_0 + \beta_1 X_i + Z_i + \cdots + e_i}$$\end{document}
where Yi is the dependent variable (number of submissions, acceptance rate, or rejection rate), Xi is the gender of the individual (the fixed effect), and Zi is the rank of the individual (random effect variable).
Recoded categories
The academic rank variable was created by consulting the respondents’ answers on the questions regarding academic rank (for those respondents that indicated their primary sector affiliation as Academia), and position (for those respondents from the Government, Private, or Not_for_Profit sector). Responses were recategorized into junior, senior, and non-academic, with unknowns removed from analyses, as per Appendix 1—table 1.
Recategorized academic rank.
Questionnaire categories
New categories
Count
Graduate student
Junior
991
Postdoctoral fellow
Research Associate
Assistant Professor
Associate Professor
Senior
3012
Full Professor
Emeritus Professor
Non-academic respondents
Non-academic
737
No answer and other
Unknowns
65
Due to limited responses, analysis per journal was not always viable. The results pertaining to the journals were aggregated, with new categories based on the shared similarities in disciplinary foci of the journals and their prestige. The categories are as follows:
Science, Nature, and PNAS (S.N.P.)
Nature Communications and Science Advances (NC.SA.)
NEJM and Cell (NEJM.C.)
Similarly, the various options for research areas in the questionnaire were reclassified into three broader areas that share common research and dissemination practices, namely natural science and engineering (NSE), medical science (MS), and social sciences (SS), as shown in Appendix 1—table 2.
Recategorized research areas to disciplines.
Disciplinary area
Field
Surveyed
Respondents
Analytical sample
Medical Sciences
Biomedical Research
63,335
832
678
Clinical Medicine
152,056
1394
1134
Health
12,807
245
208
Natural Sciences and Engineering
Biology
47,539
670
543
Chemistry
36,830
395
321
Earth and Space
36,573
576
467
Engineering and Technology
61,746
559
406
Mathematics
18,674
275
206
Physics
46,694
592
475
Social Sciences
Professional Fields
3480
82
66
Psychology
9885
243
207
Social Sciences
4354
114
94
Arts and Humanities
Arts
291
9
/
Humanities
475
14
/
Unknown
Unknown
42
2
0
Total
494,781
6002
4805
Description of the analytical sample
The Indiana University Centre for Survey Research (CSR) was contracted to administer the survey. The description of the survey response is based on the technical report produced by the CSR. After data cleaning (e.g., removing duplicates), a total of 489,777 email invitations were sent, and 6002 individuals participated in the online survey. The responses consisted of 4857 completed questionnaires. After reviewing the complete questionnaires, responses of authors in Arts (8) and humanities (11) questionnaires were excluded. Lastly, we further removed additional responses due to lack of information for critical variables (e.g., gender and discipline). An analysis of the attrition failed to identify a common point of departure, suggesting individual variability in dropout rather than failed survey construction. The final analytical sample consists of 4805 respondents. Depending on the variable, the number of responses for analyzing them may vary.
The response rate of the survey was 1.2%, as classified according to The American Association for Public Opinion Research, 2015. Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys. 8th edition (AAPOR), as summarized in Appendix 1—table 3.
Summary of response rate of questionnaire adapted from McGill University – Gender Ethnicity Publishing Study Methods Summary technical report (Indiana University Centre for Survey Research 2019).
McGill University – Gender Ethnicity Publishing Study Methods Summary. Technical Report. Unpublished.
Disposition
Definition
Count
Response rate (RR2)
Complete (I)
Respondent completed survey
4944
1.2%
Partial (P)
Respondent answered at least one question item but did not complete survey
1058
Implicit Refusal (R)
Respondent clicked survey link but did not answer any items
1655
Nothing Returned (UH)
Respondent did not respond to survey; it is unknown if any email messages were read
374,341
Undeliverable (UO)
Recruitment message was not received by intended recipient due to email and/or mailing returns
108,163
Not Eligible
Sample member indicated they were not eligible to participate; also includes cases of authors who have deceased since their publication listed in the database
58
Total
490,219
Appendix 1—tables 4–6 provide a description of the population, respondents, and analytical sample by gender.
Gender composition of population, respondents, and analysis sample.
Domain
Population/surveyed
Respondents
Analytical sample
N
%
N
%
N
%
Men
286,571
57.9
3508
58.4
2832
58.9
Women
129,592
26.2
1774
29.6
1435
29.9
Unknown
78,618
15.9
720
12.0
538
11.2
All
494,781
100.0
6002
100.0
4805
100.0
Discipline composition of population, respondents, and analysis sample.
Domain
Population/surveyed
Respondents
Analytical sample
N
%
N
%
N
%
MS
228,198
46.1
2471
41.2
2020
42.0
SS
17,719
3.6
439
7.3
366
7.6
NSE
248,098
50.1
3069
51.1
2419
50.3
AH
766
0.2
23
0.4
0.0
All disciplines
494,781
100.0
6002
100.0
4805
100.0
Discipline, gender, and academic rank composition of analytical sample.
MS (N = 2020)
NSE (N = 2419)
SS (N = 366)
Rank/role
Women
Men
Women
Men
Women
Men
N
%
N
%
N
%
N
%
N
%
N
%
Junior
200
24.5
228
18.9
147
27.6
360
19.1
31
18.7
25
12.5
Senior
465
57.0
762
63.3
281
52.7
1219
64.6
124
74.7
161
80.5
Non-academic
131
16.1
189
15.7
100
18.8
294
15.6
10
6.0
13
6.5
Unknown
20
2.5
25
2.1
5
0.9
13
0.7
1
0.6
1
0.5
Limitations
Several limitations should be noted regarding the findings of this study. First, the survey response rate is less than 2%, which may limit the generalizability of the findings. In addition, asking researchers about their behavior and intentions regarding their manuscript submissions retroactively can be complicated. There is a concern related to the capacity of researchers to remember their motivations at the time of submission; researchers with longer careers—and therefore a higher number of papers—may have submitted to various journals for various reasons. This would also make it difficult to ascertain the reasons why they do not submit to the journals, as the motivations would be different for each paper. Moreover, reasons for not submitting to these journals were not exhaustive, as noted by the various comments made by respondents. One of the notable reasons given by respondents that relate to the gender component of the study is that they did not submit to these journals due to the low percentage of women on the journal editorial boards. Based on respondents’ comments, page fees and article processing charges (APC) were also a serious consideration for some authors when deciding where to publish their work. Unfortunately, this consideration was not incorporated into the listed reasons in the questionnaire.
There are also several factors pertaining to the nature of most cited papers that complicate the interpretation of the results. For example, some respondents stated that their most cited paper was an invited paper of some kind, thus the choice of journal was already made for them and, therefore, the survey questions were irrelevant. In addition, even though their most cited paper was used as a proxy of a high-quality paper, the paper in question was not necessarily a highly cited paper, or necessarily significantly more cited than their other papers, or a paper of high quality (e.g., all their other papers could have been cited once and their most cited paper twice). That being said, the papers analyzed had, on average, a research impact 2.3 times higher than the world average of their field, which shows that they can be categorized as high impact. On the whole, this was the best proxy available for assignment of high-quality papers to researchers, and the effect is reduced by only investigating the subset of prolific researchers.
Some limitations relate to the passage of time between manuscript submission and data collection. Academic rank refers to the academic rank of the author at the time of the survey and not at the time of manuscript submission. This could result in a scenario where a respondent’s highest cited paper could have been submitted in 2008 while they were a junior academic but in 2019, at the time of the survey, they have since changed academic rank to senior academic. Therefore, this should be interpreted as the maximum rank reached by the respondent. Review policies of journals are also a potential limitation as data pertaining to the review policies at the time of manuscript submission were not collected. If a journal had a double-blind review policy at the time of submission, it might reduce potential gender bias and be a confounding effect if journals of different peer review types were grouped together. It is likely that the review policy of the journals was single-blind at the time of submission of the papers as it is the review policy most used in the fields of life sciences, physical sciences, and engineering (Ware, M. (2008). "Peer review in scholarly journals: Perspective of the scholarly community — an international study." Information Services & Use, 28(2). doi:10.5555/1454388.1454399) and thus unlikely to have changed. While this could be a factor when we investigated acceptance rate, it is not a consideration when investigating desk rejections as none of the journals include an editor-blinded review policy.
Regression and additional descriptive tables
The questionnaire was divided into four parts, and the following sections are ordered by the sections of the questionnaire. The sections are as follows:
Part 1: Consent for Research Study Participation (information regarding questionnaire – no questions)
Part 2: Your manuscript submissions (i.e., questions pertaining to submissions in general)
Part 3: Your journal article (i.e., questions pertaining to most cited paper)
Part 4: Demographics (as well as a question pertaining to quality of research in general and open comments)
Part 2: Submissions in generalJournal submission behavior
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts submitted’—percentage by discipline, journal, and gender ever submitted.
S.N.P.
Women
Men
No
Yes
No response
%Yes
No
Yes
No response
%Yes
MS
504
303
9
37.5%
555
636
13
53.4%
NSE
328
194
11
37.2%
1000
860
26
46.2%
SS
109
55
2
33.5%
110
87
3
44.2%
All disciplines
941
552
22
37.0%
1665
1583
42
48.7%
NC.SA.
Women
Men
No
Yes
No response
%Yes
No
Yes
No response
%Yes
MS
670
137
9
17.0%
902
283
19
23.9%
NSE
429
91
13
17.5%
1476
375
35
20.3%
SS
158
5
3
3.1%
181
14
5
7.2%
All disciplines
1257
233
25
15.6%
2559
672
59
20.8%
NEJM.C
Women
Men
No
Yes
No response
%Yes
No
Yes
No response
%Yes
MS
603
204
9
25.3%
795
390
19
32.9%
NSE
505
12
16
2.3%
1816
34
36
1.8%
SS
152
11
3
6.7%
182
13
5
6.7%
All disciplines
1260
227
28
15.3%
2793
437
60
13.5%
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts submitted’—odds ratio (women to men) values for the probability of submitting.
Journal
Discipline
Odds ratio
Std. Err.
z
p > |z|
95% CI lower
95% CI upper
obs
S.N.P.
MS
0.54
0.05
–6.45
0.000
0.45
0.65
1937
NSE
0.70
0.07
–3.40
0.001
0.58
0.86
2356
SS
0.62
0.14
–2.13
0.033
0.40
0.96
355
All disciplines
0.61
0.04
–7.32
0.000
0.54
0.70
4648
NC.SA.
MS
0.65
0.08
–3.68
0.000
0.52
0.82
1931
NSE
0.81
0.11
–1.60
0.110
0.63
1.05
2346
SS
0.35
0.19
–1.91
0.056
0.12
1.02
352
All disciplines
0.70
0.06
–4.06
0.000
0.59
0.83
4629
NEJM.C.
MS
0.71
0.07
–3.35
0.001
0.58
0.87
1931
NSE
1.22
0.42
0.59
0.558
0.63
2.39
2342
SS
1.08
0.46
0.18
0.859
0.47
2.50
352
All disciplines
0.75
0.07
–2.95
0.003
0.62
0.91
4625
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts submitted’—mean number of manuscript submissions.
Average number of submissions to S.N.P.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
303
3.60
3.51
1
34
636
5.12
5.41
1
35
NSE
194
3.04
2.38
1
13
860
3.95
3.95
1
30
SS
55
2.80
3.36
1
19
87
4.22
5.49
1
30
All disciplines
552
3.32
3.15
1
34
1583
4.43
4.71
1
35
Average number of submissions to NC.SA.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
137
1.74
1.34
1
10
281
1.90
1.24
1
8
NSE
89
1.65
1.08
1
6
374
1.86
1.57
1
17
SS
5
1.00
0.00
1
1
14
1.71
1.14
1
5
All disciplines
231
1.69
1.23
1
10
14
1.71
1.14
1
5
Average number of submissions to NEJM.C.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
203
1.89
1.34
1
10
389
2.25
2.00
1
16
NSE
12
1.67
1.44
1
6
34
1.76
1.48
1
8
SS
11
1.64
0.92
1
4
13
1.15
0.38
1
2
All disciplines
226
1.86
1.32
1
10
436
2.18
1.95
1
16
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts submitted’—mixed linear regression.
Journal
Discipline
Coefficient
Std. dev.
t
p > |t|
95% CI lower
95% CI upper
obs
S.N.P.
MS
–1.46
0.34
–4.26
0.000
–2.14
–0.79
921
NSE
–0.80
0.30
–2.69
0.007
–1.39
–0.22
1042
SS
–1.35
0.84
–1.61
0.110
–3.00
0.31
140
All disciplines
–1.19
0.22
–5.41
0.000
–1.62
–0.76
2103
NC.SA.
MS
–0.19
0.13
–1.41
0.161
–0.45
0.08
411
NSE
–0.19
0.18
–1.09
0.276
–0.54
0.16
459
SS
–0.71
0.54
–1.32
0.207
–1.86
0.44
19
All disciplines
–0.20
0.11
–1.89
0.059
–0.42
0.01
889
NEJM.C.
MS
–0.36
0.15
–2.39
0.017
–0.65
–0.06
575
NSE
–0.08
0.50
–0.17
0.867
–1.10
0.93
46
SS
0.59
0.32
1.84
0.081
–0.08
1.26
24
All disciplines
–0.31
0.14
–2.23
0.026
–0.58
–0.04
645
Acceptance rate
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts accepted’—mean acceptance rate.
Acceptance rate of S.N.P.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
262
0.30
0.35
0
1
565
0.30
0.34
0
1
NSE
177
0.25
0.35
0
1
798
0.24
0.34
0
1
SS
53
0.28
0.38
0
1
80
0.24
0.36
0
1
All disciplines
492
0.28
0.35
0
1
1443
0.26
0.34
0
1
Acceptance rate of NC.SA.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
137
0.27
0.39
0
1
281
0.32
0.39
0
1
NSE
89
0.30
0.41
0
1
374
0.36
0.42
0
1
SS
5
0.20
0.45
0
1
14
0.24
0.42
0
1
All disciplines
231
0.28
0.40
0
1
669
0.34
0.41
0
1
Acceptance rate of NEJM.C.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
202
0.27
0.38
0
1
388
0.26
0.36
0
1
NSE
12
0.17
0.33
0
1
34
0.18
0.34
0
1
SS
11
0.39
0.47
0
1
13
0.04
0.14
0
0.5
All disciplines
225
0.27
0.38
0
1
435
0.25
0.36
0
1
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts accepted’—linear regression.
Journal
Discipline
Coefficient
Std. dev.
t
p > |t|
95% CI lower
95% CI upper
obs
S.N.P.
MS
0.00
0.03
0.01
0.989
–0.05
0.05
810
NSE
0.01
0.03
0.48
0.628
–0.04
0.07
964
SS
0.05
0.07
0.78
0.435
–0.08
0.18
132
All disciplines
0.01
0.02
0.57
0.566
–0.03
0.05
1906
NC.SA.
MS
–0.05
0.04
–1.15
0.252
–0.13
0.03
411
NSE
–0.07
0.05
–1.42
0.156
–0.17
0.03
459
SS
–0.04
0.22
–0.16
0.877
–0.51
0.44
19
All disciplines
0.06
0.03
–1.78
0.075
–0.12
0.01
889
NEJM.C.
MS
0.02
0.03
0.65
0.517
–0.04
0.08
573
NSE
–0.02
0.11
–0.15
0.885
–0.25
0.21
46
SS
0.43
0.15
2.79
0.011
0.11
0.74
24
All disciplines
0.03
0.03
1.07
0.287
–0.03
0.09
643
Desk rejections
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts rejected’—mean desk rejection rate.
Desk rejection rate of S.N.P.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
262
0.76
0.34
0
1
565
0.79
0.28
0
1
NSE
172
0.81
0.31
0
1
764
0.79
0.29
0
1
SS
46
0.85
0.26
0
1
75
0.87
0.24
0
1
All disciplines
480
0.79
0.32
0
1
1404
0.80
0.28
0
1
Desk rejection rate of NC.SA.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
105
0.80
0.34
0
1
210
0.77
0.35
0
1
NSE
64
0.72
0.38
0
1
266
0.74
0.37
0
1
SS
4
0.75
0.50
0
1
11
0.86
0.25
0
1
All disciplines
173
0.77
0.36
0
1
487
0.76
0.36
0
1
Desk rejection rate of NEJM.C.
Women
Men
Obs
Mean
Std. dev.
Min
Max
Obs
Mean
Std. dev.
Min
Max
MS
161
0.68
0.40
0
1
320
0.67
0.40
0
1
NSE
9
0.72
0.44
0
1
28
0.71
0.43
0
1
SS
6
0.83
0.26
0.5
1
13
0.88
0.30
0
1
All disciplines
176
0.68
0.39
0
1
361
0.68
0.40
0
1
Question: ‘For each of the journals listed in the table below, please indicate the appropriate number of manuscripts rejected’—linear regression.
Journal
Discipline
Coefficient
Std. dev.
t
p > |t|
95% CI lower
95% CI upper
obs
S.N.P.
MS
–0.03
0.02
–1.33
0.185
–0.07
0.01
809
NSE
0.03
0.03
1.01
0.314
–0.02
0.07
928
SS
0.00
0.05
–0.09
0.928
–0.09
0.09
119
All disciplines
0.00
0.02
–0.31
0.757
–0.04
0.03
1856
NC.SA.
MS
0.04
0.04
0.96
0.336
–0.04
0.12
311
NSE
–0.03
0.05
–0.61
0.542
–0.14
0.07
326
SS
–0.14
0.20
–0.71
0.494
–0.59
0.30
15
All disciplines
0.01
0.03
0.17
0.867
–0.06
0.07
652
NEJM.C.
MS
0.02
0.04
0.37
0.708
–0.06
0.09
467
NSE
0.03
0.17
0.18
0.862
–0.32
0.38
37
SS
–0.09
0.17
–0.53
0.607
–0.45
0.27
19
All disciplines
0.01
0.04
0.33
0.741
–0.06
0.09
523
Reasons for not considering submitting to top journals
Question: ‘Please indicate the reason(s) why you did not consider submitting a manuscript to journal name taken from response to the previous question’—number of respondents and percentage selecting response by discipline and gender.
Reasons for not submitting to S.N.P.
MS
NSE
SS
Women
Men
Women
Women
Men
Women
Total
%Yes
Total
%Yes
Total
%Yes
Total
%Yes
Total
%Yes
Total
%Yes
Work was not of high enough quality
501
36%
553
41%
328
37%
998
27%
109
33%
110
30%
Work fell outside the scope of the journal
501
61%
553
55%
328
46%
998
49%
109
72%
110
59%
Work was not groundbreaking or sufficiently novel
501
52%
553
50%
328
53%
998
44%
109
53%
110
40%
Work would fit better in a more specialized journal
501
56%
553
63%
328
64%
998
70%
109
58%
110
69%
Work would reach a wider audience in another journal
501
9%
553
13%
328
16%
998
17%
109
7%
110
8%
Co-authors wished to submit the manuscript elsewhere
501
7%
553
7%
328
9%
998
5%
109
6%
110
5%
I was advised against submitting to this journal
501
6%
553
4%
328
8%
998
5%
109
6%
110
5%
Reasons for not submitting to NC.SA.
MS
NSE
SS
Women
Men
Women
Women
Men
Women
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
Work was not of high enough quality
667
22%
894
23%
427
21%
1467
15%
157
12%
180
17%
Work fell outside the scope of the journal
667
46%
894
37%
427
33%
1467
34%
157
61%
180
53%
Work was not groundbreaking or sufficiently novel
667
32%
894
29%
427
37%
1467
28%
157
29%
180
23%
Work would fit better in a more specialized journal
667
42%
894
47%
427
53%
1467
52%
157
39%
180
51%
Work would reach a wider audience in another journal
667
9%
894
14%
427
13%
1467
17%
157
10%
180
11%
Co-authors wished to submit the manuscript elsewhere
667
9%
894
9%
427
10%
1467
8%
157
6%
180
4%
I was advised against submitting to this journal
667
4%
894
4%
427
3%
1467
3%
157
3%
180
2%
Reasons for not submitting to NEJM.C.
MS
NSE
SS
Women
Men
Women
Women
Men
Women
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
Work was not of high enough quality
602
34%
791
31%
503
6%
1801
4%
151
15%
180
8%
Work fell outside the scope of the journal
602
83%
791
80%
503
84%
1801
86%
151
93%
180
90%
Work was not groundbreaking or sufficiently novel
602
38%
791
33%
503
8%
1801
5%
151
21%
180
12%
Work would fit better in a more specialized journal
602
37%
791
35%
503
13%
1801
13%
151
24%
180
27%
Work would reach a wider audience in another journal
602
5%
791
7%
503
3%
1801
4%
151
5%
180
6%
Co-authors wished to submit the manuscript elsewhere
602
5%
791
6%
503
2%
1801
2%
151
5%
180
3%
I was advised against submitting to this journal
602
4%
791
4%
503
0%
1801
0%
151
2%
180
1%
Question: ‘Please indicate the reason(s) why you did not consider submitting a manuscript to journal name taken from response to the previous question’—odds ratio (women to men) values.
Reasons why did not consider submitting papers to top journals
S.N.P.
NC.SA.
NEJM.C.
MS
NSE
SS
All displ.
MS
NSE
SS
All displ.
MS
NSE
SS
All displ.
Work was not of high enough quality
0.83
1.59†
1.04
1.12
0.91
1.51†
0.60
1.08
1.10
1.51
1.69
1.21
Work fell outside the scope of the journal
1.27
0.94
1.84*
1.14
1.42†
1.01
1.41
1.24†
1.20
0.92
1.41
1.08
Work was not groundbreaking or sufficiently novel
1.24*
1.47†
1.69*
1.26†
1.13
1.41†
1.27
1.26†
1.18
1.60*
1.71
1.31†
Work would fit better in a more specialized journal
0.74*
0.72*
0.58
0.72*
0.82
1.02
0.60*
0.87
1.06
1.01
0.81
1.01
Work would reach a wider audience in another journal
0.61*
0.93
0.92
0.78
0.58†
0.74
0.90
0.68†
0.71
0.81
0.80
0.76
Co-authors wished to submit the manuscript elsewhere
1.02
1.70*
1.31
1.32
1.00
1.17
1.34
1.09
0.88
1.09
1.34
0.97
I was advised against submitting to this journal
1.30
1.69*
1.37
1.48*
0.87
0.77
1.73
0.87
1.24
1.11
1.52
1.25
I was unaware of this journal
‡No one reported they were unaware of the top journals
indicates p < 0.05.
indicates p < 001.
Insufficient sample.
Part 3: Most citedJournal submission behavior
Question: ‘For your published manuscript <Title of the respondent’s most cited paper>, did you ever consider submitting it to a journal other than <the title of the journal in which it was published>?’—percentage by discipline, and gender.
Discipline
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
370
432
802
53.9%
559
616
1175
52.4%
NSE
273
248
521
47.6%
1038
814
1852
44.0%
SS
80
84
164
51.2%
101
92
193
47.7%
All disciplines
723
764
1487
51.4%
1698
1522
3220
47.3%
Question: ‘For your published manuscript <Title of the respondent’s most cited paper>, did you ever consider submitting it to a journal other than <the title of the journal in which it was published>?’—odds ratio (women to men) values.
Other journals
F/M odds ratio
Std. err
z
p > |z|
95% CI lower
95% CI upper
obs
MS
1.07
0.100
0.67
0.50
0.89
1.28
1914
NSE
1.12
0.113
1.14
0.26
0.92
1.37
2351
SS
1.16
0.250
0.68
0.50
0.76
1.77
351
All disciplines
1.10
0.072
1.40
0.16
0.96
1.25
4616
Question: ‘Before publication of your manuscript,<Title of the respondent’s most cited paper>, did you ever consider submitting it to the journals listed in the table below?’—percentage by discipline, journal, and gender.
Consider submitting to S.N.P.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
721
76
797
9.5%
1029
143
1172
12.2%
NSE
478
40
518
7.7%
1702
148
1850
8.0%
SS
157
6
163
3.7%
185
13
198
6.6%
All disciplines
1356
122
1478
8.3%
2916
304
3220
9.4%
Consider submitting to NC.SA.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
743
32
775
4.1%
1082
46
1128
4.1%
NSE
486
17
503
3.4%
1747
47
1794
2.6%
SS
161
1
162
0.6%
194
0
194
0.0%
All disciplines
1390
50
1440
3.5%
3023
93
3116
3.0%
Consider submitting to NEJM.C.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
749
42
791
5.3%
1078
73
1151
6.3%
NSE
500
2
502
0.4%
1788
3
1791
0.2%
SS
163
0
163
0.0%
192
2
194
1.0%
All disciplines
1412
44
1456
3.0%
3058
78
3136
2.5%
Question: ‘Before publication of your manuscript,<Title of the respondent’s most cited paper>, did you ever consider submitting it to the journals listed in the table below?’—odds ratio (women to men) values.
Journal
W/M odds ratio
Std. err
z
p > |z|
95% CI lower
95% CI upper
obs
S.N.P.
MS
0.79
0.120
–1.53
0.126
0.59
1.07
1907
NSE
0.96
0.181
–0.21
0.83
0.66
1.39
2342
SS
0.54
0.275
–1.21
0.227
0.20
1.47
355
All disciplines
0.83
0.096
–1.59
0.112
0.66
1.04
4604
NC.SA.
MS
1.01
0.240
0.05
0.961
0.64
1.61
1844
NSE
1.05
0.321
0.16
0.873
0.58
1.91
2272
SS
Insufficient sample
All disciplines
1.05
0.196
0.25
0.801
0.73
1.51
4466
NEJM.C.
MS
0.84
0.170
–0.87
0.386
0.56
1.25
1882
NSE
2.44
2.246
0.97
0.333
0.40
14.84
2268
SS
Insufficient sample
All disciplines
0.85
0.166
–0.85
0.393
0.57
1.24
4501
Question: ‘Before publication of your manuscript,<Title of the respondent’s most cited paper>, did you ever submit it to the journals listed in the table below?’—percentage by discipline, journal, and gender.
Submitted to S.N.P.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
37
38
75
50.7%
58
81
139
58.3%
NSE
22
17
39
43.6%
64
80
144
55.6%
SS
1
4
5
80.0%
5
8
13
61.5%
All disciplines
60
59
119
49.6%
127
169
296
57.1%
Submitted to NC.SA.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
19
14
33
42.4%
25
27
52
51.9%
NSE
13
7
20
35.0%
35
21
56
37.5%
SS
1
0
1
0.0%
0
0
0
0
All disciplines
33
21
54
38.9%
60
48
108
44.4%
Submitted to NEJM.C.
Women
Men
No
Yes
Total
%Yes
No
Yes
Total
%Yes
MS
29
13
42
31.0%
40
31
71
43.7%
NSE
2
0
2
0.0%
2
0
2
0.0%
SS
0
0
0
0
2
0
2
0.0%
All disciplines
31
13
44
29.5%
44
31
75
41.3%
Question: ‘Before publication of your manuscript, <Title of the respondent’s most cited paper>, did you ever submit it to the journals listed in the table below?’—odds ratio (women to men) values for the probability of submitting.
Journal
Discipline
W/M odds ratio
Std. err
z
p > |z|
95% CI lower
95% CI upper
obs
S.N.P.
MS
0.75
0.22
–0.99
0.321
0.43
1.32
210
NSE
0.65
0.24
–1.19
0.234
0.31
1.33
180
SS
2.50
3.14
0.73
0.465
0.21
29.25
18
All disciplines
0.74
0.17
–1.35
0.177
0.48
1.15
408
NC.SA.
MS
0.69
0.32
–0.82
0.414
0.28
1.70
84
NSE
1.06
0.61
0.10
0.918
0.35
3.25
73
SS
No one submitted to NC.SA.
All disciplines
0.80
0.29
–0.61
0.540
0.40
1.62
157
NEJM.C.
MS
0.55
0.23
–1.42
0.157
0.24
1.26
111
NSE
No one submitted to NEJM.C.
SS
No one submitted to NEJM.C.
All disciplines
0.57
0.24
–1.35
0.178
0.25
1.29
117
Desk rejections
Question: ‘Was your manuscript <Title of the respondent’s most cited paper> sent out for peer review before being rejected by the journals listed in the table below?’—percentage by discipline, journal, and gender.
Sent out for peer review S.N.P.
Women
Men
No
Yes
Total
%DeskRej
No
Yes
Total
%DeskRej
MS
26
10
36
72.2%
62
19
81
76.5%
NSE
14
3
17
82.4%
58
22
80
72.5%
SS
3
1
4
75.0%
8
0
8
100.0%
All disciplines
43
14
57
75.4%
128
41
169
75.7%
Sent out for peer review NC.SA.
Women
Men
No
Yes
Total
%DeskRej
No
Yes
Total
%DeskRej
MS
12
2
14
85.7%
21
7
28
75.0%
NSE
7
1
8
87.5%
17
4
21
81.0%
SS
No responses
All disciplines
19
3
22
86.4%
31
11
49
77.6%
Sent out for peer review NEJM.C.
Women
Men
No
Yes
Total
%DeskRej
No
Yes
Total
%DeskRej
MS
5
8
13
38.5%
27
5
32
84.4%
NSE
No responses
SS
Question: ‘Was your manuscript <Title of the respondent’s most cited paper> sent out for peer review before being rejected by the journals listed in the table below?’—odds ratio (women to men) values.
Journal
Discipline
W/M odds ratio
Std. err
z
p > |z|
95% CI lower
95% CI upper
obs
S.N.P.
MS
0.48
0.66
0.74
0.46
0.56
3.52
114
NSE
1.41
0.33
–1.05
0.29
0.12
1.88
95
SS
Insufficient sample
All disciplines
1.13
0.41
0.32
0.75
0.55
2.31
221
NC.SA.
MS
0.43
0.38
–0.95
0.34
0.07
2.46
36
NSE
0.70
0.90
–0.28
0.78
0.06
8.82
27
SS
No responses
All disciplines
0.50
0.37
–0.95
0.35
0.12
2.10
63
NEJM.C.
MS
13.07
10.96
3.07
0.00
2.53
67.60
44
NSE
No responses
SS
Reasons for not considering submitting to top journals
Question: ‘Please indicate the reason(s) why you did not consider submitting your manuscript <Title of the respondent’s most cited paper> to the journals listed below.’—number of respondents and percentage selecting response by discipline and gender.
Reasons for not submitting to S.N.P.
MS
NSE
SS
Women
Men
Women
Men
Women
Men
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
Work was not of high enough quality
719
25%
1027
30%
475
21%
1696
18%
156
27%
185
18%
Work fell outside the scope of the journal
719
45%
1027
44%
475
35%
1696
40%
156
62%
185
60%
Work was not groundbreaking or sufficiently novel
719
45%
1027
44%
475
48%
1696
39%
156
39%
185
30%
Work would fit better in a more specialized journal
719
53%
1027
53%
475
60%
1696
63%
156
46%
185
57%
Work would reach a wider audience in another journal
719
8%
1027
9%
475
12%
1697
14%
156
8%
185
11%
Co-authors wished to submit the manuscript elsewhere
719
7%
1027
6%
475
8%
1697
4%
156
6%
185
3%
I was advised against submitting to this journal
719
5%
1027
3%
475
3%
1696
2%
156
2%
185
2%
Reasons for not submitting to NC.SA.
MS
NSE
SS
Women
Men
Women
Women
Men
Women
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
Work was not of high enough quality
732
19%
1076
22%
481
14%
1731
13%
159
14%
189
12%
Work fell outside the scope of the journal
732
41%
1076
38%
481
31%
1731
33%
159
63%
189
58%
Work was not groundbreaking or sufficiently novel
732
34%
1076
32%
481
38%
1731
30%
159
29%
189
21%
Work would fit better in a more specialized journal
732
46%
1076
45%
481
52%
1731
55%
159
32%
189
43%
Work would reach a wider audience in another journal
732
8%
1076
11%
481
10%
1731
12%
159
5%
189
7%
Co-authors wished to submit the manuscript elsewhere
732
6%
1076
6%
481
6%
1732
5%
159
8%
189
2%
I was advised against submitting to this journal
732
2%
1076
2%
481
2%
1731
2%
159
1%
189
1%
Reasons for not submitting to NEJM.C.
MS
NSE
SS
Women
Men
Women
Women
Men
Women
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
n
%Yes
Work was not of high enough quality
738
25%
1073
28%
495
5%
1771
5%
161
15%
187
7%
Work fell outside the scope of the journal
738
72%
1073
75%
495
85%
1772
86%
161
91%
187
86%
Work was not groundbreaking or sufficiently novel
738
37%
1073
36%
495
9%
1771
7%
161
18%
187
11%
Work would fit better in a more specialized journal
738
44%
1073
42%
495
16%
1771
18%
161
25%
187
30%
Work would reach a wider audience in another journal
738
6%
1073
6%
495
4%
1771
4%
161
4%
187
7%
Co-authors wished to submit the manuscript elsewhere
738
5%
1073
4%
495
2%
1771
1%
161
5%
187
2%
I was advised against submitting to this journal
738
2%
1073
2%
495
0%
1771
0%
161
1%
187
0%
Question: ‘Please indicate the reason(s) why you did not consider submitting your manuscript <Title of the respondent’s most cited paper> to the journals listed below.’—odds ratio (women to men) values.
Reasons why did not consider submitting the most cited papers to top journals
S.N.P.
NC.SA.
NEJM.C.
MS
NSE
SS
All
MS
NSE
SS
All
MS
NSE
SS
All
Work was not of high enough quality
0.75*
1.17
1.63
0.96
0.86
1.00
1.31
0.93
0.83
0.94
2.36*
0.91
Work fell outside the scope of the journal
1.05
0.82
1.15
0.95
1.09
0.93
1.30
1.04
0.86
0.94
1.52
0.92
Work was not groundbreaking or sufficiently novel
1.02
1.39 ‡
1.48
1.19*
1.05
1.28*
1.81
1.14
1.05
1.28
1.81
1.14
Work would fit better in a more specialized journal
0.99
0.88
0.66
0.90
1.00
0.90
0.64
0.92
1.08
0.92
0.79
1.00
Work would reach a wider audience in another journal
0.96
0.87
0.71
0.89
0.72
0.87
0.76
0.79
0.98
0.85
0.55
0.88
Co-authors wished to submit the manuscript elsewhere
1.13
1.57*
2.26
1.35*
0.99
1.06
4.53*
1.12
1.14
1.41
2.07
1.26
I was advised against submitting to this journal
1.75*
1.51
0.97
1.59*
0.96
1.06
2.27
1.03
1.47
3.52
+
1.67
I was unaware of this journal
†No one reported they were unaware of the top journals
indicates p < 0.05.
Insufficient sample.
indicates p < 0.01.
Part 4: Perception of the quality of research
Question: ‘Compared to my peers, I feel that the quality of my research is’—average rank and count by discipline and gender.
Women
Discipline
Average quality rank
Std. dev
#Good and Excellent
#Average
#Fair and Poor
Total
MS
4.2
0.69
574
74
8
656
NSE
4.0
0.67
352
60
8
420
SS
4.1
0.62
135
18
0
153
All disciplines
4.1
0.68
1061
152
16
1229
Men
Discipline
Average quality rank
Std. dev
#Good and Excellent
#Average
#Fair and Poor
Total
MS
4.1
0.7
841
126
7
974
NSE
4.2
0.6
1363
185
9
1557
SS
4.1
0.8
151
25
6
182
All disciplines
4.1
0.7
2355
336
22
2713
Note: Excellent (5)–Poor (1).
Question: ‘Compared to my peers, I feel that the quality of my research is’—ordinal logistic (women to men), controlled for rank.
This convincing study, which is based on a survey of researchers, finds that women are less likely than men to submit articles to elite journals. It also finds that there is no relation between gender and reported desk rejection. The study is an important contribution to work on gender bias in the scientific literature.
10.7554/eLife.90049.4.sa1Joint Public Review:Reviewer
Summary from an earlier round of review:
This paper summarises responses from a survey completed by around 5,000 academics on their manuscript submission behaviours. The authors find several interesting stylised facts, including (but not limited to):- Women are less likely to submit their papers to highly influential journals (e.g., Nature, Science and PNAS).
- Women are more likely to cite the demands of co-authors as a reason why they didn’t submit to highly influential journals.
- Women are also more likely to say that they were advised not to submit to highly influential journals.
The paper highlights an important point, namely that the submission behaviours of men and women scientists may not be the same (either due to preferences that vary by gender, selection effects that arise earlier in scientists’ careers or social factors that affect men and women differently and also influence submission patterns). As a result, simply observing gender differences in acceptance rates - or a lack thereof - should not be automatically interpreted as as evidence for or against discrimination (broadly defined) in the peer review process.
Editor’s note: This is the third version of this article.
Comments made during the peer review of the second version, along with author’s responses to these comments, are available below. Revisions made in response to these comments include changing the colour scheme used for the figures to make the figures more accessible for readers with certain forms of colour blindness.
Comments made during the peer review of the first version, along with author’s responses to these comments, are available with previous versions of the article.
10.7554/eLife.90049.4.sa2Author responseNiChaoqunAuthorUniversity of Wisconsin-MadisonMadisonUnited StatesBassonIsabelAuthorUniversité de MontréalMontrealCanadaBadiaGiovannaAuthorMcGill UniversityMontrealCanadaTufenkjiNathalieAuthorMcGill UniversityMontréalCanadaSugimotoCassidy RAuthorGeorgia Institute of TechnologyAtlantaUnited StatesLarivièreVincentAuthorUniversité de MontréalMontrealCanada
The following is the authors’ response to the previous reviews
Reviewer #1 (Public review):
Summary
This paper summarises responses from a survey completed by around 5,000 academics on their manuscript submission behaviours. The authors find several interesting stylised facts, including (but not limited to):
Women are less likely to submit their papers to highly influential journals (e.g., Nature, Science and PNAS).
Women are more likely to cite the demands of co-authors as a reason why they didn't submit to highly influential journals.
Women are also more likely to say that they were advised not to submit to highly influential journals.
The paper highlights an important point, namely that the submission behaviours of men and women scientists may not be the same (either due to preferences that vary by gender, selection effects that arise earlier in scientists' careers or social factors that affect men and women differently and also influence submission patterns). As a result, simply observing gender differences in acceptance rates - or a lack thereof - should not be automatically interpreted as as evidence for or against discrimination (broadly defined) in the peer review process.
Major comments
What do you mean by bias?
In the second paragraph of the introduction, it is claimed that "if no biases were present in the case of peer review, then we should expect the rate with which members of less powerful social groups enjoy successful peer review outcomes to be proportionate to their representation in submission rates." There are a couple of issues with this statement.
First, the authors are implicitly making a normative assumption that manuscript submission and acceptance rates *should* be equalised across groups. This may very well be the case, but there can also be valid reasons - even when women are not intrinsically better at research than men - why a greater fraction of female-authored submissions are accepted relative to male-authored submissions (or vice versa). For example, if men are more likely to submit their less ground-breaking work, then one might reasonably expect that they experience higher rejection rates compared to women, conditional on submission.
We do assume that normative statement: unless we believe that men’s papers are intrinsically better than women’s papers, the acceptance rate should be the same. But the referee is right: we have no way of controlling for the intrinsic quality of the work of men and women. That said, our manuscript does not show that there is a different acceptance rate for men and women; it shows that women are less likely to submit papers to a subset of journals that are of a lower Journal Impact Factor, controlling for their most cited paper, in an attempt to control for intrinsic quality of the manuscripts.
Second, I assume by "bias", the authors are taking a broad definition, i.e., they are not only including factors that specifically relate to gender but also factors that are themselves independent of gender but nevertheless disproportionately are associated with one gender or another (e.g., perhaps women are more likely to write on certain topics and those topics are rated more poorly by (more prevalent) male referees; alternatively, referees may be more likely to accept articles by authors they've met before, most referees are men and men are more likely to have met a given author if he's male instead of female). If that is the case, I would define more clearly what you mean by bias. (And if that isn't the case, then I would encourage the authors to consider a broader definition of "bias"!)
Yes, the referee is right that we are taking a broad definition of bias. We provide a definition of bias on page 3, line 92. This definition is focused on differential evaluation which leads to differential outcomes. We also hedge our conversation (e.g., page 3, line 104) to acknowledge that observations of disparities may only be an indicator of potential bias, as many other things could explain the disparity. In short, disparities are a necessary but insufficient indicator of bias. We add a line in the introduction to reinforce this. The only other reference to the term bias comes on page 10, line 276. We add a reference to Lee here to contextualize.
Identifying policy interventions is not a major contribution of this paper
I would take out the final sentence in the abstract. In my opinion, your survey evidence isn't really strong enough to support definitive policy interventions to address the issue and, indeed, providing policy advice is not a major - or even minor - contribution of your paper. (Basically, I would hope that someone interested in policy interventions would consult another paper that much more thoughtfully and comprehensively discusses the costs and benefits of various interventions!) While it's fine to briefly discuss them at the end of your paper - as you currently do - I wouldn't highlight that in the abstract as being an important contribution of your paper.
We thank the referee for this comment. While we agree that our results do not lead to definitive policy interventions, we believe that our findings point to a phenomenon that should be addressed through policy interventions. Given that some interventions are proposed in our conclusion, we feel like stating this in the abstract is coherent.
Minor comments
What is the rationale for conditioning on academic rank and does this have explanatory power on its own - i.e., does it at least superficially potentially explain part of the gender gap in intention to submit?
Thank you for this thoughtful question. We conditioned on academic rank in all regression analyses to account for structural differences in career stage that may potentially influence submission behaviors. Academic rank (e.g., assistant, associate, full professor) is a key determinant of publishing capacity and strategic considerations, such as perceived likelihood of success at elite journals, tolerance for risk, and institutional expectations for publication venues.
Importantly, academic rank is also correlated with gender due to cumulative career disadvantages that contribute to underrepresentation of women at more senior levels. Failing to adjust for rank would conflate gender effects with differences attributable to career stage. By including rank as a covariate, we aim to isolate gender-associated patterns in submission behavior within comparable career stages, thereby producing a more precise estimate of the gender effect.
Regarding explanatory power, academic rank does indeed contribute significantly to model fit across our analyses, indicating that it captures meaningful variation in submission behavior. However, even after adjusting for rank, we continue to observe significant gender differences in submission patterns in several disciplines. This suggests that while academic rank explains part of the variation, it does not fully account for the gender gap—highlighting the importance of examining other structural and behavioral factors that shape the publication trajectory.
Reviewer #2 (Public review):
Basson et al. present compelling evidence supporting a gender disparity in article submission to "elite" journals. Most notably, they found that women were more likely to avoid submitting to one of these journals based on advice from a colleague/mentor. Overall, this work is an important addition to the study of gender disparities in the publishing process.
I thank the authors for addressing my concerns.
Reviewer #4 (Public review):
Main strengths
The topic of the MS is very relevant given that across the sciences/academia, genders are unevenly represented, which has a range of potential negative consequences. To change this, we need to have the evidence on what mechanisms cause this pattern. Given that promotion and merit in academia are still largely based on the number of publications and the impact factor, one part of the gap likely originates from differences in publication rates of women compared to men.
Women are underrepresented compared to men in journals with a high impact factor. While previous work has detected this gap and identified some potential mechanisms, the current MS provides strong evidence that this gap might be due to a lower submission rate of women compared to men, rather than the rejection rates. These results are based on a survey of close to 5000 authors. The survey seems to be conducted well (though I am not an expert in surveys), and data analysis is appropriate to address the main research aims. It was impossible to check the original data because of the privacy concerns.
Interestingly, the results show no gender bias in rejection rates (desk rejection or overall) in three high-impact journals (Science, Nature, PNAS). However, submission rates are lower for women compared to men, indicating that gender biases might act through this pathway. The survey also showed that women are more likely to rate their work as not groundbreaking and are advised not to submit to prestigious journals, indicating that both intrinsic and extrinsic factors shape women's submission behaviour.
With these results, the MS has the potential to inform actions to reduce gender bias in publishing, but also to inform assessment reform at a larger scale.
I do not find any major weaknesses in the revised manuscript.
Reviewer #4 (Recommendations for the authors):
(1) Colour schemes of the Figures are not adjusted for colour-blindness (red-green is a big NO), some suggestions can be found here https://www.nceas.ucsb.edu/sites/default/files/2022-06/Colorblind%20Safe%20Color%20Schemes.pdf
We appreciate the suggestion. We’ve adjusted the colors in the manuscript to be color-blind friendly using one of the colorblind safe palettes suggested by the reviewer.
(2) I do not think that the authors have fully addressed the comment about APCs and the decision to submit, given that PNAS has publication charges that amount to double of someone's monthly salary. I would add a sentence or two to explain that publication charges should not be a factor for Nature and Science, but might be for PNAS.
While APCs are definitely a factor affecting researchers’ submission behavior, it is mostly does so for lower prestige journals rather than for the three elite journals analyzed here. As mentioned in the previous round of revisions, Nature and Science have subscription options. And PNAS authors without funding have access to waivers: https://www.pnas.org/author-center/publication-charges
(3) Line 268, the first suggestion here is not something that would likely work. Thus, I would not put it as the first suggestion.
We made the suggested change.
(4) Data availability - remove AND in 'Aggregated and de-identified data' because it sounds like both are shared. Suggest writing: 'Aggregated, de-identified data..'. I still suggest sharing data/code in a trusted repository (e.g. Dryad, ZENODO...) rather than on GitHub, as per the current recommendation on the best practices for data sharing.
Thank you for your comment regarding data availability. Due to IRB restrictions and the conditions of our ethics approval, we are not permitted to share the survey data used in this study. However, to support transparency and reproducibility, we have made all analysis code available on Zenodo at https://doi.org/10.5281/zenodo.16327580. In addition, we have included a synthetic dataset with the same structure as the original survey data but containing randomly generated values. This allows others to understand the data structure and replicate our analysis pipeline without compromising participant confidentiality.